Paytm—Deputy General Manager- Product Analyst Lead

Job description

Description: About the Role UPI is the backbone of India's digital payments revolution — and Paytm is one of its largest participants, processing hundreds of millions of transactions every month. We are looking for an Analytics Lead to join our UPI team and own the data and insights charter that powers product, growth, and platform decisions across our UPI-powered flows.This role sits at the intersection of consumer behaviour, financial infrastructure, and large-scale data systems. You will build the analytical backbone for UPI — from funnel and cohort frameworks to experimentation infrastructure — and work closely with product, engineering, data science, and business teams to turn transaction-scale data into decisions that affect millions of Indians daily. Key Responsibilities 1. Analytics Strategy & OwnershipOwn the end-to-end analytics charter for one or more UPI surfaces — from metric definition to instrumentation to insight generationBuild and maintain the source-of-truth framework for north-star and guardrail metrics across the UPI funnel (initiation, authentication, success/failure, retention)Partner with NPCI-mandated reporting requirements and ensure analytical rigor aligns with regulatory data standardsLead a team of analysts, setting priorities, reviewing outputs, and raising the bar on analytical craf t2. Funnel, Growth & Behavioural InsightsDiagnose friction points across the UPI journey using deep-dive analysis, cohort studies, and funnel decompositionBuild engagement, frequency, and top-of-wallet models to inform product and growth strategyDesign analytical frameworks for discovery and nudge effectiveness as new UPI capabilities are adoptedPartner with design and product research to combine quantitative and qualitative signals into a single narrative 3. Platform Reliability & Scale AnalyticsDefine and monitor SLA, success-rate, and latency benchmarks; build alerting and anomaly-detection frameworks tied to these metricsBuild root-cause analysis playbooks for payment failure spikes and incident-linked drop-offsModel traffic and failure patterns ahead of high-volume events (festive seasons, IPL, government disbursements) to support platform readiness 4. Experimentation & Decision ScienceOwn the experimentation backbone — design, power, and evaluate A/B tests across the UPI funnelDrive a test-and-learn culture: hypothesis → experiment → read-out → scale, with statistically sound methodologyBuild self-serve dashboards and tooling that let product, engineering, and business teams access trusted insights without analyst dependencyProactively surface insights that shape upstream roadmap and prioritization decisions Required Qualifications 7–9 years of analytics/data experience, with a mandatory focus on consumer-facing products or fintech; prior experience in payments or UPI strongly preferred Strong command of SQL and hands-on experience with product analytics tools (Mixpanel, Amplitude, Metabase, or equivalent); working knowledge of Python/R for deeper statistical analysis Solid grounding in experimentation design — hypothesis framing, sample size/power calculations, and interpreting A/B results correctlyProven ability to translate ambiguous business questions into structured analyses and clear, decision-ready recommendations Experience building and owning dashboards and metrics frameworks at scale, including data quality and governance practices Strong written and verbal communication skills — ability to present findings crisply to leadership and align cross-functional stakeholders Experience managing or mentoring a team of analysts, with a demonstrated ability to review work and raise analytical standards Good to Have Prior experience working in a UPI-regulated environment or with NPCI data/reporting constructs Exposure to large-scale transaction data systems and familiarity with data warehousing/pipeline concepts (e.g., working with data engineering teams on instrumentation) Experience with growth, lifecycle, or CRM-driven analytics Familiarity with fraud/risk analytics in a payments context